基于STM32的植物固碳环境智能监测与自动化控制系统设计

Design of an Intelligent Monitoring and Automated Control System for Plant Carbon Sequestration Environment Based on STM32

摘要: 随着“双碳”战略的不断推进，植物固碳能力研究逐渐成为智慧农业与生态环境监测领域的重要研究方向。传统植物固碳环境监测主要依赖人工测量与实验室分析，不仅监测效率较低，而且难以实现环境参数的长期连续采集。针对传统植物固碳监测系统自动化程度不足、实时性较差以及环境调节能力有限等问题，文章设计了一种基于STM32的植物固碳环境智能监测与自动化控制系统。系统以STM32F103C8T6单片机作为核心控制器，结合BH1750光照传感器、SHT30高精度温湿度传感器以及JW01二氧化碳检测模块，实现植物生长环境参数的实时采集。相比传统DHT11传感器，SHT30具有更高检测精度与长期稳定性，可有效提高植物固碳环境监测可靠性，并通过OLED显示模块完成本地数据显示，同时利用ESP8266无线通信模块实现远程数据上传。为了提高系统自动化控制能力，文章进一步引入PID闭环控制算法，对植物生长环境中的温度、湿度、光照以及二氧化碳浓度进行动态调节，并建立环境状态模型与自动化控制模型。利用MATLAB建立系统动态仿真模型，对系统动态响应特性、误差收敛特性以及环境鲁棒性能进行了分析。实验结果表明，该系统具有较好的动态调节能力与环境适应能力，系统光照检测误差小于2%，温度稳定时间小于8 s，在复杂环境条件下仍能够保持较高稳定性。研究结果对于智能农业、低碳生态环境监测以及自动化控制系统研究具有一定参考价值。

Abstract: With the continuous advancement of the “dual carbon” strategy, research on plant carbon sequestration capacity has gradually become an important research direction in the fields of smart agriculture and ecological environment monitoring. Traditional plant carbon sequestration environment monitoring mainly relies on manual measurement and laboratory analysis, which not only has low monitoring efficiency but also makes it difficult to achieve long-term continuous acquisition of environmental parameters. To address the problems of insufficient automation, poor real-time performance, and limited environmental regulation capabilities in traditional plant carbon sequestration monitoring systems, this paper designs an intelligent monitoring and automated control system for plant carbon sequestration environment based on STM32. The system uses the STM32F103C8T6 microcontroller as the core controller, combined with a BH1750 light sensor, an SHT30 high-precision temperature and humidity sensor, and a JW01 carbon dioxide detection module to achieve real-time acquisition of plant growth environment parameters. Compared to the traditional DHT11 sensor, the SHT30 offers higher detection accuracy and long-term stability, effectively improving the reliability of plant carbon sequestration environment monitoring. Local data display is achieved via an OLED module, while remote data upload is realized using an ESP8266 wireless communication module. To enhance the system’s automated control capabilities, this paper further introduces a PID closed-loop control algorithm to dynamically adjust temperature, humidity, light intensity, and carbon dioxide concentration in the plant growth environment. An environmental state model and an automated control model are established. A dynamic simulation model of the system is built using MATLAB, and the system’s dynamic response characteristics, error convergence characteristics, and environmental robustness are analyzed. Experimental results show that the system possesses good dynamic adjustment capabilities and environmental adaptability. The system’s light detection error is less than 2%, and the temperature stabilization time is less than 8 seconds, maintaining high stability even under complex environmental conditions. These research results provide valuable reference for research in intelligent agriculture, low-carbon ecological environment monitoring, and automated control systems.